- author = "Paul Kaufmann and Marco Platzner",
- title = "Multi-objective Intrinsic Hardware Evolution",
- booktitle = "Intl. Conf. Military Applications of Programmable Logic Devices (MAPLD)",
- year = "2006",
- editor = "Richard Katz",
- pages = "Submission 210",
- address = "Washington, D.C, USA",
- month = sep # " 26-28",
- organisation = "NASA",
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming:Poster",
-
URL = "
http://klabs.org/mapld06/abstracts/210_kaufmann_a.html",
- broken = "http://www.cs.uni-paderborn.de/fileadmin/Informatik/AG-Platzner/publications/kaufmann06_mapld/mapld2006kaufmann.pdf",
-
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.541.3122",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.541.3122.pdf",
- size = "7 pages",
-
abstract = "Computer Engineering Group A robust embedded system
has to adapt properly not only to changes in the
environment but also to changes in the available
resources. As an example, an autonomously moving
vehicle might suddenly need to assign most of its
computing resources to navigation, leaving less
resources than anticipated for other tasks.
Evolutionary techniques are well-suited to adapt to
slow changes. For rapid changes, however, the speed of
convergence of the evolutionary algorithm is not
sufficient to react properly. While we envision
environmental changes as rather slow, changes in the
available resources are considered more rapid. In our
project, we are concerned with intrinsically evolvable
digital hardware. Besides their functional quality, the
evolved hardware functions typically have objectives
such as the required logic area, the maximum operation
speed and the power consumption. These objectives are
often conflicting and cannot be optimized
simultaneously. A trade-off has to be found between the
different objectives.
In this paper, we present a novel approach to evolvable embedded systems that is able to adapt to both slow and radical changes in the environment and the system state, respectively. First, a multi-objective evolutionary search algorithm with a selection scheme based on Pareto dominance is used to compute a set of reasonable trade-offs. Then, the decision is made which solution to use for the present situation. During operation, the systems adapts to slowly changing environmental conditions by the evolutionary search process. To handle radical changes, precomputed dominant solutions are stored in the system. When a radical change occurs, the system switches to a good-enough solution, and the online evolutionary process is restarted.
We will present details of the Cartesian Genetic Programming model used, the evolutionary technique, and the evaluation of the fitness with respect to several objectives. We will demonstrate our approach on two classes of applications. The first class of applications reveals an exact correctness measure, where everything less than 100percent correctness is unacceptable. For such a scenario, treating the fitness as a constraint during the optimization process is a viable possibility. The second class of applications relies on a continuous fitness measure, such as the quality of a predictor inside an image compressing algorithm. For such a scenario, the functional quality is best handled as an objective.",
-
notes = "SPEA2, TSPEA2, 6-parity function and a hashing
function
http://klabs.org/mapld06/index.htm",
